
from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowercase    :   Dict						=			200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowercase    :   str						=			50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowercase    :   List[str]						=			0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str		,    _lowerCamelCase					:     str)   ->       tuple[str, float]:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   len([g for position, g in enumerate(_lowerCamelCase) if g == main_target[position]])
							return (item, float(_lowerCamelCase))




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str		,    _lowerCamelCase					:     str)   ->       tuple[str, str]:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   random.randint(0		,    len(_lowerCamelCase) - 1)
							__UpperCamelCase      :  Optional[Any]       =   parent_a[:random_slice] + parent_a[random_slice:]
							__UpperCamelCase      :  List[str]       =   parent_a[:random_slice] + parent_a[random_slice:]
							return (child_a, child_a)




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str		,    _lowerCamelCase					:     list[str])   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  List[Any]       =   list(_lowerCamelCase)
							if random.uniform(0		,    1) < MUTATION_PROBABILITY:
														__UpperCamelCase      :  List[str]       =   random.choice(_lowerCamelCase)
							return "".join(_lowerCamelCase)




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     tuple[str, float]		,    _lowerCamelCase					:     list[tuple[str, float]]		,    _lowerCamelCase					:     list[str]		,    )   ->       list[str]:






							'''simple docstring'''





							__UpperCamelCase      :  List[Any]       =   []
							# Generate more children proportionally to the fitness score.
							__UpperCamelCase      :  Dict       =   int(parent_a[1] * 100) + 1
							__UpperCamelCase      :  Optional[Any]       =   10 if child_n >= 10 else child_n
							for _ in range(_lowerCamelCase):
														__UpperCamelCase      :  str       =   population_score[random.randint(0		,    _lowerCamelCase)][0]

														__UpperCamelCase  ,		__UpperCamelCase      :  int       =   crossover(parent_a[0]		,    _lowerCamelCase)
														# Append new string to the population list.
														pop.append(mutate(_lowerCamelCase		,    _lowerCamelCase))
														pop.append(mutate(_lowerCamelCase		,    _lowerCamelCase))
							return pop




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str		,    _lowerCamelCase					:     list[str]		,    _lowerCamelCase					:     bool = True)   ->       tuple[int, int, str]:






							'''simple docstring'''





							if N_POPULATION < N_SELECTED:
														__UpperCamelCase      :  str       =   F'{N_POPULATION} must be bigger than {N_SELECTED}'
														raise ValueError(_lowerCamelCase)
							# Verify that the target contains no genes besides the ones inside genes variable.
							__UpperCamelCase      :  Tuple       =   sorted({c for c in target if c not in genes})
							if not_in_genes_list:
														__UpperCamelCase      :  Dict       =   F'{not_in_genes_list} is not in genes list, evolution cannot converge'
														raise ValueError(_lowerCamelCase)

							# Generate random starting population.
							__UpperCamelCase      :  List[str]       =   []
							for _ in range(_lowerCamelCase):
														population.append("".join([random.choice(_lowerCamelCase) for i in range(len(_lowerCamelCase))]))

							# Just some logs to know what the algorithms is doing.
							__UpperCamelCase  ,		__UpperCamelCase      :  Union[str, Any]       =   0, 0

							# This loop will end when we find a perfect match for our target.
							while True:
														generation += 1
														total_population += len(_lowerCamelCase)

														# Random population created. Now it's time to evaluate.

														# Adding a bit of concurrency can make everything faster,
														#
														# import concurrent.futures
														# population_score: list[tuple[str, float]] = []
														# with concurrent.futures.ThreadPoolExecutor(
														#                                   max_workers=NUM_WORKERS) as executor:
														#     futures = {executor.submit(evaluate, item) for item in population}
														#     concurrent.futures.wait(futures)
														#     population_score = [item.result() for item in futures]
														#
														# but with a simple algorithm like this, it will probably be slower.
														# We just need to call evaluate for every item inside the population.
														__UpperCamelCase      :  int       =   [evaluate(_lowerCamelCase		,    _lowerCamelCase) for item in population]

														# Check if there is a matching evolution.
														__UpperCamelCase      :  List[Any]       =   sorted(_lowerCamelCase		,    key=lambda _lowerCamelCase: x[1]		,    reverse=_lowerCamelCase)
														if population_score[0][0] == target:
																					return (generation, total_population, population_score[0][0])

														# Print the best result every 10 generation.
														# Just to know that the algorithm is working.
														if debug and generation % 10 == 0:
																					print(
																					    F'\nGeneration: {generation}'
																					    F'\nTotal Population:{total_population}'
																					    F'\nBest score: {population_score[0][1]}'
																					    F'\nBest string: {population_score[0][0]}')

														# Flush the old population, keeping some of the best evolutions.
														# Keeping this avoid regression of evolution.
														__UpperCamelCase      :  str       =   population[: int(N_POPULATION / 3)]
														population.clear()
														population.extend(_lowerCamelCase)
														# Normalize population score to be between 0 and 1.
														__UpperCamelCase      :  Union[str, Any]       =   [
														    (item, score / len(_lowerCamelCase)) for item, score in population_score
														]

														# This is selection
														for i in range(_lowerCamelCase):
																					population.extend(select(population_score[int(_lowerCamelCase)]		,    _lowerCamelCase		,    _lowerCamelCase))
																					# Check if the population has already reached the maximum value and if so,
																					# break the cycle.  If this check is disabled, the algorithm will take
																					# forever to compute large strings, but will also calculate small strings in
																					# a far fewer generations.
																					if len(_lowerCamelCase) > N_POPULATION:
																												break


if __name__ == "__main__":
		lowercase    :   Tuple						=			(
		    'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'
		)
		lowercase    :   Dict						=			list(
		    ' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
		    'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
		)
		lowercase				,     lowercase				,     lowercase    :   Union[str, Any]						=			basic(target_str, genes_list)
		print(
		    f"\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"
		)

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[int]		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  str       =   BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase      :  Optional[Any]       =   BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,    _lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Union[str, Any]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Any						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)